基于多维测量信息的压缩感知多目标无源被动定位算法

无源被动定位是入侵者检测、环境监测以及智能交通等应用的关键问题之一。现有的无源被动定位方法可通过信道状态信息获取多个维度上的测量信息,但是现有方案未能充分挖掘多个信道上的频率分集以提高定位性能。该文提出一种基于多维测量信息的压缩感知多目标无源被动定位算法,在压缩感知框架下利用多维测量信息的频率分集提高定位精度和鲁棒性。根据鞍面模型建立无源字典,将多目标无源被动定位问题建模成多测量向量联合稀疏恢复问题,并利用多维稀疏贝叶斯学习算法估计目标位置向量。仿真结果表明,该算法能有效利用多维测量信息提高定位性能。

     

Lexicon-based approach outperforms Supervised Machine Learning approach for Urdu Sentiment Analysis in multiple domains

Web is facilitating people to express their views and opinions on different topics through reviews and blogs. Effective advantages can be reaped from these reviews and blogs by fusing the sentiment knowledge. In this research, Sentiment Analysis of Urdu blogs from multiple domains is done by using the two widely used approaches i.e. the Lexicon-based approach and the Supervised Machine Learning approach. Three well known classifiers i.e. Support Vector Machine, Decision Tree and K Nearest Neighbor are used in case of Supervised Machine Learning approach whereas a wide coverage Urdu Sentiment Lexicon and an efficient Urdu Sentiment Analyzer are used in Lexicon-based approach. In both the approaches the information are fused from two sources to successfully perform Sentiment Analysis. In case of Lexicon-based approach, the two sources are the wide coverage Urdu Sentiment Lexicon and the efficient Urdu Sentiment Analyzer. In case of Supervised Machine Learning approach, the two sources are the un-annotated data and annotated data along with important attributes. After performing Sentiment Analysis using both the approaches, the results are observed carefully and on the basis of experiments performed in this research, it is concluded that the Lexicon-based approach outperforms Supervised Machine Learning approach not only in terms of Accuracy, Precision, Recall and F-measure but also in terms of economy of time and efforts used.     

基于CDIO模式的高职技师课程开发探讨

CDIO模式为高职院校技师课程体系开发提供了参考模型和标准。本文对照CDIO的模型、大纲、标准和评价对课程开发、课程设计、课程实施和课程评价分别进行分析,特别强调了课程要以具体的三级实践项目贯穿教学,培养学生的实际职业能力。     

神经网络法汉语孤立音识别的研究

本文对神经网络法语音识别中的网络结构、特征提取以及学习算法进行了初步的研究.文中提出了一种带非均匀窗形式的金字塔状多层神经网络模型I研究了两种特征提取方法的向量规整问题,改进了Polak—Rjbiere学习算法并证明它能够保证连结权向量不收敛到非稳定局部极小点.特定人方式的试验表明,用神经网络方法识别五个元音和十个数字时,识别率都高于99.0％.用遍布25个省市的90个说话者的数字话音所进行的非特定人方式试验表明;加窗后识别率可以提高1.0％,达到90.3％,说明带预处理窗的模型更加适合于语音识别.     

广义知识存储原理与高阶广义神经网络

本文在仔细分析神经网络知识存储方式的基础上，提出了高阶广义神经网络的概念和构成原则，讨论了其中一种典型的智能神经元模糊以及据此实现高阶广义神经网络的各项性能，最后将其与标准的ＢＰ算法进行了比较。     

Evaluation of hip fracture risk using a hyper-parametric model based on the Locally Linear Embedding technique

The hip fracture is one of the most common diseases for elder people and also, one of the most worrying one since it usually is the starting point of further complications for both, the health of the patient and their daily life. Additionally, reports shown that there exist differences between people living in different regions, thus limiting the use of global models. In this work we propose a hip fracture prediction tool for a local region, using clinical data of the population of that region. The data is processed with a dimensionality reduction tool in combination with and hyper-parametrization process and the corresponding hyper-parameter optimization process for obtaining good predictions in the diagnoses, as the results shown.     

Extending the participatory learning paradigm to include source credibility

We provide an overview of the participatory learning paradigm (PLP) and discuss the importance of the acceptance function in determining which observations are used for learning. We introduce a formal model that uses this (PLP) We then extend this model in two directions. First, we consider situations in which we have incomplete observations, we only have observations about a subset of the variables of interest. Next we extend this model to allow for the inclusion in the learning process of information about the learning agents belief about the credibility of the source of the learning experience. Here we distinguish between the content of a learning experience and the source of the experience. We provide a means to allow the learning agents belief about the credibility of the source to determine the effect of the content. Furthermore we suggest a method to allow the modification of agents belief about the credibility of the source to also be part of the learning process.